Fine-Tuning LLMs Or Zero/Few-Shot Prompting for Knowledge Graph Construction? - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Fine-Tuning LLMs Or Zero/Few-Shot Prompting for Knowledge Graph Construction?

Résumé

This paper explores Text-to-Knowledge Graph (T2KG) construction" assessing Zero-Shot Prompting (ZSP), Few-Shot Prompting (FSP), and Fine-Tuning (FT) methods with Large Language Models (LLMs). Through comprehensive experimentation with Llama2, Mistral, and Starling, we highlight the strengths of FT, emphasize dataset size's role, and introduce nuanced evaluation metrics. Promising perspectives include synonym-aware metric refinement, and data augmentation with LLMs. The study contributes valuable insights to KG construction methodologies, setting the stage for further advancements.
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Dates et versions

hal-04862214 , version 1 (02-01-2025)

Identifiants

  • HAL Id : hal-04862214 , version 1

Citer

Hussam Ghanem, Christophe Cruz. Fine-Tuning LLMs Or Zero/Few-Shot Prompting for Knowledge Graph Construction?. French Regional Conference on Complex Systems, May 2024, Montpellier, France. ⟨hal-04862214⟩
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